"""simple docstring"""



def 	_lowerCamelCase(	a = 1   ,    a = 1_0_0_0				):
       __a       =    1
       __a       =    0

       for divide_by_number in range(a   ,    digit + 1				):
              __a       =    []
              __a       =    numerator
              for _ in range(1   ,    digit + 1				):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(a				):
                                   __a       =    len(a				)
                                   __a       =    divide_by_number
                     else:
                            has_been_divided.append(a				)
                            __a       =    now_divide * 1_0 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    (list, tuple)				) and isinstance(videos[0]   ,    (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(a   ,    (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(a				):
              return [[videos]]

       raise ValueError(F"Could not make batched video from {videos}"				)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)
        __a       =    size if size is not None else {"shortest_edge": 256}
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else {"height": 224, "width": 224}
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        __a       =    do_resize
        __a       =    size
        __a       =    do_center_crop
        __a       =    crop_size
        __a       =    resample
        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    offset
        __a       =    do_normalize
        __a       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        if "shortest_edge" in size:
               __a       =    get_resize_output_image_size(lowerCamelCase    ,		size["shortest_edge"]    ,		default_to_square=lowerCamelCase						)
        elif "height" in size and "width" in size:
               __a       =    (size["height"], size["width"])
        else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
        return resize(lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase						)
        if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
        return center_crop(lowerCamelCase    ,		size=(size["height"], size["width"])    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    image.astype(np.floataa						)
        if offset:
               __a       =    image - (scale / 2)
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        return normalize(lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		):
        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        __a       =    to_numpy_array(lowerCamelCase						)

        if do_resize:
               __a       =    self.resize(image=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase						)

        if do_center_crop:
               __a       =    self.center_crop(lowerCamelCase    ,		size=lowerCamelCase						)

        if do_rescale:
               __a       =    self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase    ,		offset=lowerCamelCase						)

        if do_normalize:
               __a       =    self.normalize(image=lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase						)

        __a       =    to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						)
        return image






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_resize if do_resize is not None else self.do_resize
        __a       =    resample if resample is not None else self.resample
        __a       =    do_center_crop if do_center_crop is not None else self.do_center_crop
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    offset if offset is not None else self.offset
        __a       =    do_normalize if do_normalize is not None else self.do_normalize
        __a       =    image_mean if image_mean is not None else self.image_mean
        __a       =    image_std if image_std is not None else self.image_std

        __a       =    size if size is not None else self.size
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else self.crop_size
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        __a       =    make_batched(lowerCamelCase						)

        __a       =    [
            [
                self._preprocess_image(
                    image=lowerCamelCase    ,		do_resize=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		do_center_crop=lowerCamelCase    ,		crop_size=lowerCamelCase    ,		do_rescale=lowerCamelCase    ,		rescale_factor=lowerCamelCase    ,		offset=lowerCamelCase    ,		do_normalize=lowerCamelCase    ,		image_mean=lowerCamelCase    ,		image_std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		)
                for img in video
            ]
            for video in videos
        ]

        __a       =    {"pixel_values": videos}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)
